Understand the top 4 use cases for AI in cybersecurity

Cybersecurity technology and risks evolve in lockstep with AI. Today, companies must train machine learning algorithms to recognize attacks perpetrated Other threat actors have developed smart malware — or even artificial hackers — to personalize attacks tailored to victims’ specific contexts. AI-based attacks demonstrate AI’s common value propositions: rapid scalability, behavioral analytics and personalization. These capabilities can be used nefariously in breaches, outbreaks or other security incidents.

The enterprise hacker cat-and-mouse game represents an important and dangerous dynamic in cybersecurity innovation. It remains critical that organizations wield investment to protect, especially as legacy systems cannot be easily updated or replaced.

The above use cases are but a few of the numerous applications for AI in cybersecurity. For all the potential, machine learning is not a silver bullet; it is a just a tool. And remember: Avoid silver bullet thinking, but consider the silver lining. Despite vendors’ lofty marketing, the reality is that enterprise security landscapes are vast, dynamic networks. They must be constantly monitored, audited and updated based on ongoing unpredictable internal and external threat vectors. To define what is anomalous requires defining what is normal. This is extremely difficult, as computing and economic environments transform so rapidly.

While traditional signature-based methods of threat detection — not to mention humans — have blind spots, so too do machine learning techniques. Clear intention for application is paramount for any tool, and the output is only as good as the data input. Finally, as with any action-reaction, there is cause for optimism: Ever more sophisticated threats are sparking a renaissance of ever more sophisticated mitigation tools.